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Search Results (462)

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Keywords = methane adsorption

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18 pages, 7579 KiB  
Article
Molecular Simulation of Ultra-Microstructural Characteristics of Adsorption Pores in Terms of Coal and Gas Adsorption Properties
by Pan Chen, Yanping Wang, Yanxia Zhao, Qi Wang, Zhihui Wen and Ligang Tang
Processes 2025, 13(3), 771; https://doi.org/10.3390/pr13030771 (registering DOI) - 7 Mar 2025
Abstract
To investigate the ultra-microstructural characteristics and adsorption properties of coal pores, the pore structure of Dongsheng lignite and Chengzhuang anthracite in Qinshui Basin was characterized by the liquid nitrogen adsorption method. It was found that the SSA of micropores constituted more than 65% [...] Read more.
To investigate the ultra-microstructural characteristics and adsorption properties of coal pores, the pore structure of Dongsheng lignite and Chengzhuang anthracite in Qinshui Basin was characterized by the liquid nitrogen adsorption method. It was found that the SSA of micropores constituted more than 65% of the total SSA in both coal samples. The macromolecular model of coal and the N2 molecular probe were used to obtain the ultrastructure parameters, and the gas adsorption behaviors of the two coals under different conditions were simulated by Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD). The results show that the pores of the lignite are mainly small pores, while the pores of the anthracite are mainly micropores. The specific surface area of the adsorption pores mainly constitutes micropores and ultra-micropores. The adsorption capacity of the CH4 of anthracite is consistently higher than that of lignite. The CH4 adsorption amount is positively correlated with the specific surface area and pore volume. This indicates that the gas adsorption capacity of coal is concentrated in micropores and ultra-micropores. The adsorption capacity increases with the increase in pressure and decreases with the increase in temperature. In the competitive adsorption of CH4/CO2/H2O, the adsorption quantity is in the order of H2O > CO2 > CH4. The research results provide a theoretical basis for coalbed methane exploitation and methane replacement. Full article
(This article belongs to the Special Issue Advances in Coal Processing, Utilization, and Process Safety)
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<p>(<b>a</b>) Initial 3D molecular model of DS-L. (<b>b</b>) Initial 3D molecular model of CZ-A.</p>
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<p>(<b>a</b>) Macromolecular model of DS-L under periodic boundary conditions. (<b>b</b>) Macromolecular model of CZ-A under periodic boundary conditions.</p>
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<p>Pore shapes reflected by adsorption isotherms.</p>
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<p>(<b>a</b>) Liquid nitrogen adsorption isotherms of DS-L. (<b>b</b>) Liquid nitrogen adsorption isotherms of CZ-A.</p>
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<p>Diagram of the Connolly principle for the SSA calculation.</p>
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<p>(<b>a</b>) Molecular probe result of DS-L. (<b>b</b>) Molecular probe result of CZ-A.</p>
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<p>(<b>a</b>) Saturated adsorption configuration of macromolecules for DS-L. (<b>b</b>) Saturated adsorption configuration of macromolecules for CZ-A.</p>
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<p>Isothermal adsorption curves of DS-L at different temperatures.</p>
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<p>Isothermal adsorption curves of CZ-A at different temperatures.</p>
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<p>Molecular configurations of adsorbates.</p>
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<p>(<b>a</b>) Adsorption density field of the DS-L macromolecular model after adsorbing CH<sub>4</sub>. (<b>b</b>) Adsorption density field of the DS-L macromolecular model after adsorbing CO<sub>2</sub>. (<b>c</b>) Adsorption density field of the DS-L macromolecular model after adsorbing H<sub>2</sub>O.</p>
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<p>(<b>a</b>) Adsorption density field of the CZ-A macromolecular model after adsorbing CH<sub>4</sub>. (<b>b</b>) Adsorption density field of the CZ-A macromolecular model after adsorbing CO<sub>2</sub>. (<b>c</b>) Adsorption density field of the CZ-A macromolecular model after adsorbing H<sub>2</sub>O.</p>
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<p>Adsorption density fields of the DS-L macromolecular model.</p>
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<p>Adsorption density fields of the CZ-A macromolecular model.</p>
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<p>Isothermal adsorption curves of DS-L for different adsorbates.</p>
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<p>Isothermal adsorption curves of CZ-A for different adsorbates.</p>
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12 pages, 3475 KiB  
Article
Research on the Microscopic Adsorption Characteristics of Methane by Coals with Different Pore Sizes Based on Monte Carlo Simulation
by Chunhua Zhang and Yuqi Zhai
Appl. Sci. 2025, 15(5), 2349; https://doi.org/10.3390/app15052349 - 22 Feb 2025
Viewed by 194
Abstract
In order to explore the influence of different pore sizes of anthracite on the methane adsorption characteristics, a low-temperature liquid nitrogen adsorption experiment was carried out. Six types of anthracite with pore sizes ranging from 10 Å to 60 Å were selected as [...] Read more.
In order to explore the influence of different pore sizes of anthracite on the methane adsorption characteristics, a low-temperature liquid nitrogen adsorption experiment was carried out. Six types of anthracite with pore sizes ranging from 10 Å to 60 Å were selected as simulation objects. By means of molecular simulation technology and using the Materials Studio 2020 software, a macromolecular model of anthracite was established, and a grand canonical Monte Carlo (GCMC) simulation comparative study was conducted. The variation laws of the interaction energy and diffusion during the process of coal adsorbing CH4 under different pore size conditions were obtained. The results show that affected by the pore size, under the same temperature condition, the peak value of the interaction energy distribution between coal and CH4 shows a downward trend with the increase in the pore size under the action of pressure, and the energy gradually decreases. The isothermal adsorption curves all conform to the Langmuir isothermal adsorption model. The Langmuir adsorption constant a shows an obvious upward trend with the increase in the pore size, with an average increase of 16.43%. Moreover, under the same pressure, when the pore size is 60 Å, the adsorption amount of CH4 is the largest, and as the pore size decreases, the adsorption amount also gradually decreases. The size of the pore size is directly proportional to the diffusion coefficient of CH4. When the pore size increases to 50 Å, the migration state of CH4 reaches the critical point of transformation, and the diffusion coefficient rapidly increases to 2.3 times the original value. Full article
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<p>Construction and optimization of anthracite macromolecule model.</p>
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<p>Pore structure model of coal with different pore sizes.</p>
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<p>Interaction energy distribution of coal with CH4 for different pore sizes.</p>
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<p>Isothermal adsorption curves of coal with different pore size pores and CH<sub>4</sub>.</p>
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<p>Fitting results of CH<sub>4</sub> rms displacement in different coal pore sizes.</p>
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15 pages, 2978 KiB  
Article
Effect of Vacuum Process on Enrichment of Low-Concentration Coal Mine Methane by Adsorption
by Yuanyuan Kang, Yingshu Liu, Wenhai Liu, Ye Li, Ningqi Sun, Quanli Zhang, Ziyi Li and Xiong Yang
Separations 2025, 12(3), 56; https://doi.org/10.3390/separations12030056 - 20 Feb 2025
Viewed by 256
Abstract
The massive emission of low-concentration coal mine methane (CMM) has resulted in the ineffective utilization of a large amount of energy methane and caused environmental pollution. The gas mixture used in the study consisted of methane (CH4) 12% and nitrogen (N [...] Read more.
The massive emission of low-concentration coal mine methane (CMM) has resulted in the ineffective utilization of a large amount of energy methane and caused environmental pollution. The gas mixture used in the study consisted of methane (CH4) 12% and nitrogen (N2) 88%. The adsorbent was coconut activated carbon. This paper uses the adsorption method to conduct enrichment research on 12% low-concentration CMM. Firstly, the variation in methane gas concentration under different desorption methods was studied by numerical simulation, and the desorption methods suitable for increasing methane concentration were analyzed. A three-bed VPSA CMM separation experimental device was built, and three enrichment processes of feed gas pressurization, exhaust gas pressurization, and vacuum exhaust (VE) were studied. The results show that using the three-bed vacuum pressure swing adsorption (VPSA) process can effectively enrich low-concentration CMM. Under the adsorption pressure of 110 kPa and the desorption pressure of 10 kPa, 12% of CMM can be enriched to more than 25%, with a recovery rate higher than 80%. The exhaust process can significantly increase the product gas concentration. The product gas concentration increased by 18.2%, with the product rising from 22.5% to 26.6% when the extraction step increased from 0 s to 8 s. This research may provide reliable fundamental data for industrial-scale low-concentration CMM enrichment. Full article
(This article belongs to the Section Separation Engineering)
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<p>Isotherm of active carbon.</p>
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<p>Schematic diagram of the experimental equipment system for the penetration process. 1—gas tank; 2—mass flow controller; 3—valve; 4—actived carbon bed; 5—thermal resistance; 6—pressure transmitters; 7—constant temperature water bath; 8—vacuum pump; 9—back pressure valve; 10—vacuum pressure gauge; 11—mass spectrometer; 12—A/D card; 13—computer.</p>
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<p>Schematic diagram of experimental setup. 1—compressor; 2—buffer tank; 3—mass flowmeter; 4—solenoid valve; 5—adsorber; 6—gasbag; 7—check valve; 8—hand valve; 9—methane analyzer; 10—vacuum pump; 11—tank; PI—pressure sensor.</p>
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<p>Schedule of VPSA on 1/3 of cycle ((<b>a</b>) pressurization with feed; (<b>b</b>) pressurization with effluent gas; (<b>c</b>) pressurization with effluent gas combined with vacuum exhaust).</p>
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<p>Numerical simulation comparison of breakthrough.</p>
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<p>A comparison of the methane content of desorption from the inlet and outlet of the adsorption bed after adsorption 60 s: (<b>a</b>) the pressure curve during the desorption step, (<b>b</b>) the effect of desorption pressure on methane content, (<b>c</b>) the effect of desorption time on methane content.</p>
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<p>Comparison of methane content of desorption from inlet and outlet of adsorption bed after adsorption 80 s.</p>
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<p>Comparison of methane content of desorption from inlet and outlet of adsorption bed after adsorption 100 s.</p>
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<p>Contrast of methane gas concentration in the adsorption bed at different times of desorption (adsorption 60 s).</p>
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<p>The change in desorption gas concentration after depressurization at the outlet end.</p>
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<p>Comparison of CH<sub>4</sub> content and effluent gas flow with adsorption time in two processes: (<b>a</b>) process a, (<b>b</b>) process b.</p>
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<p>Variation in product gas concentration with exhaust gas concentration for two processes.</p>
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<p>Effect of vacuum exhaust time on product concentration and recovery.</p>
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20 pages, 9191 KiB  
Article
Identification and Application of Favorable Lithofacies Associations in the Transitional Facies of the Permian Longtan Formation in Central and Southern Sichuan Basin
by Longyi Wang, Xizhe Li, Ya’na Chen, Wei Guo, Xiangyang Pei, Chao Luo, Chong Tian, Jingyuan Zhang, Nijun Qi, Weikang He, Wenxuan Yu and Hongming Zhan
Minerals 2025, 15(3), 198; https://doi.org/10.3390/min15030198 - 20 Feb 2025
Viewed by 226
Abstract
The transitional shale system of the Longtan Formation (LTF) is widely distributed in the Sichuan Basin. However, the lithofacies of the LTF shale system exhibit vertical variations, with frequent interbedding of blocks, and shale–sand–coal sequences, which makes identifying “sweet spots” a challenging task. [...] Read more.
The transitional shale system of the Longtan Formation (LTF) is widely distributed in the Sichuan Basin. However, the lithofacies of the LTF shale system exhibit vertical variations, with frequent interbedding of blocks, and shale–sand–coal sequences, which makes identifying “sweet spots” a challenging task. To address this issue, lithofacies associations were investigated based on well log analysis from 30 wells, and experimental data from 19 well samples, including X-ray diffraction, total organic carbon (TOC), pore structure characterization, and methane isothermal adsorption tests. Four lithofacies associations were classified: carbon–shale interbedding (I-1), shale(carbon)–coal interbedding (I-2), shale–sand interbedding (II), and shale–sand–coal assemblage (III). A favorable lithofacies association index (Com) was developed, providing a quantitative method for identifying favorable lithofacies. The results indicate that among the four lithofacies associations, I-2 is the most favorable lithofacies association. The Com index threshold for favorable lithofacies is defined as 0.6, and for the most favorable lithofacies, it is 0.7. Overall, favorable lithofacies are primarily distributed in the Suining-Dazu and Lujiao areas. Full article
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<p>Well locations and composite stratigraphic column of the permian LTF in the Sichuan Basin study area. (<b>a</b>) Well locations. (<b>b</b>) Composite stratigraphic column.</p>
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<p>Core photos and thin-section identification photos of the LTF. (<b>a</b>) NT1H, 4366.08 m, drilled core, shale; (<b>b</b>) NT1H, 4366.08 m, ×50 (−), developed lamination, visible microcracks, unfilled; (<b>c</b>) NT1H, 4366.08 m, ×50 (+), mixed distribution of quartz, feldspar, and clay; (<b>d</b>) N242, 3016.30 m, core sample, carbonaceous shale, multiple high-angle fractures, filled with calcite; (<b>e</b>) N242, 3016.30 m, ×50 (−), visible multiple microfractures, calcite filling; (<b>f</b>) N242, 3016.30 m, ×50 (+), carbonaceous irregular shape, oriented arrangement; (<b>g</b>) GS133, 3902.22 m, core sample, coal, developed cleats of coal; (<b>h</b>) GS133, 3902.22 m, ×50 (−), laminated structure; (<b>i</b>) GS133, 3902.22 m, ×50 (+), mudstone with laminated and lenticular enrichment; (<b>j</b>) ZG1, 2934.87 m, core sample, limestone; (<b>k</b>) ZG1, 2934.87 m, ×100 (−), bioclasts with brachiopods, ostracods, foraminifera, etc.; (<b>l</b>) ZG1, 2934.87 m, ×100 (+); (<b>m</b>) LJ1, 3169.68 m, core sample, siltite, parallel lamination; (<b>n</b>) LJ1, 3169.83 m, ×50 (−); (<b>o</b>) LJ1, 3169.83 m, ×50 (+), sandstone dominated by chert, with some quartz, feldspar, and deeply altered feldspar; (<b>p</b>) ZG1, 2985.4 m, core sample, tuffaceous sandstone; (<b>q</b>) ZG1, 2985.4 m, ×100 (−), irregular liquefied sand bands in tuff; (<b>r</b>) ZG1, 2985.4 m, ×100 (+), composed mainly of lithic fragments, including tuff and carbonate rock fragments; (<b>s</b>) N242, 3016.89 m, core sample, bauxitic mudstone, visible pyrite; (<b>t</b>) N242, 3015.89 m, ×50 (−), pyrite in anhedral grains and irregular clumps; (<b>u</b>) N242, 3015.89 m, ×50 (+), silica often associated with pyrite.</p>
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<p>Lithology identification process.</p>
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<p>Flowchart for lithofacies association classification.</p>
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<p>Schematic of the lithofacies association geological parameter of the thickness weighting method.</p>
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<p>Cross-plot for lithology identification using logging data.</p>
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<p>Comparison of lithology identification results and core lithology in well MC1.</p>
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<p>Composite stratigraphic diagram of experimental testing and well logging interpretation (taken as an example of well QT1).</p>
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<p>Comparison chart of total hydrocarbon logging for different lithofacies associations.</p>
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<p>Fitting chart of total hydrocarbon logging and Com for different lithofacies associations.</p>
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<p>Violin Plot analysis of the Com index for four lithofacies assemblage types.</p>
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<p>Distribution map of dominant lithofacies assemblages in the LTF of central and southern Sichuan. (<b>a</b>) Tan-1 Member; (<b>b</b>) Tan-2 Member; (<b>c</b>) Tan-3 Member.</p>
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<p>Well correlation profile. (<b>A</b>,<b>A’</b>) QTI-TT1-ZT1-YT1; (<b>B</b>,<b>B’</b>) MC1-JF1-GS133-ZS101; (<b>C</b>,<b>C’</b>) N242-FT1-LG1-GS133-MX39.</p>
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17 pages, 6112 KiB  
Article
Adsorption and Decomposition Mechanisms of Vapor Growth Carbon Fiber on SiO2 in Non-Catalytic Conditions: A First-Principles Study
by Chen Ma, Fanguang Zeng and Shenbo Yang
Crystals 2025, 15(2), 195; https://doi.org/10.3390/cryst15020195 - 18 Feb 2025
Viewed by 260
Abstract
In this study, the authors employed first-principles calculations to investigate the adsorption and decomposition processes involved in non-catalytic growth of vapor-growth carbon fiber (VGCF) using a non-catalytic growth method. The adsorption and decomposition mechanisms of methane and its decomposition products on the substrate [...] Read more.
In this study, the authors employed first-principles calculations to investigate the adsorption and decomposition processes involved in non-catalytic growth of vapor-growth carbon fiber (VGCF) using a non-catalytic growth method. The adsorption and decomposition mechanisms of methane and its decomposition products on the substrate were investigated with the adsorption energy, transition state analysis, and projected density of states (PDOS). The results indicated that the surface adsorption difficulty for CH4 and its decomposition products followed the following order: H > CH4 ≈ CH3 > CH2 > CH > C. The adsorption energy analysis indicates that the adsorption of CH4, CH3, and H is classified as physical adsorption, whereas the adsorption of CH2, CH, and C is classified as chemical adsorption. Adsorption of all particles is exothermic and adsorption can occur. The transition state calculations indicate that the decomposition of CH4 is the rate-determining step in the decomposition reaction. PDOS analysis not only verified the results of adsorption energy analysis but also investigated the effect of adsorption particles. This work is helpful for advancing the application of non-catalytic growth processes to the synthesis of VGCF and enhancing the understanding of the mechanisms governing non-catalytic VGCF formation. Full article
(This article belongs to the Section Inorganic Crystalline Materials)
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<p>Initial model for the surface adsorption calculation. (<b>a</b>) Substrates model, (<b>b</b>) cell model: Si = gray, O = red.</p>
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<p>Top view of adsorption sites on the α-quartz SiO<sub>2</sub> (001) surface (labeled with numbers).</p>
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<p>The potential energy profile for the dehydrogenation process of particles adsorbed on the α-quartz SiO<sub>2</sub> substrate: (<b>a</b>) CH<sub>4</sub>; (<b>b</b>) CH<sub>3</sub>; (<b>c</b>) CH<sub>2</sub>; (<b>d</b>) CH.</p>
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<p>The potential energy profile for the dehydrogenation process of particles adsorbed on the α-quartz SiO<sub>2</sub> substrate: (<b>a</b>) CH<sub>4</sub>; (<b>b</b>) CH<sub>3</sub>; (<b>c</b>) CH<sub>2</sub>; (<b>d</b>) CH.</p>
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<p>The potential energy profile for the dehydrogenation process of particles adsorbed on the α-quartz SiO<sub>2</sub> substrate: (<b>a</b>) CH<sub>4</sub>; (<b>b</b>) CH<sub>3</sub>; (<b>c</b>) CH<sub>2</sub>; (<b>d</b>) CH.</p>
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<p>Energy change diagram of CH<sub>4</sub> decomposition process on the α-quartz SiO<sub>2</sub> substrate.</p>
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<p>Projected density of states (PDOS) of the α-quartz SiO<sub>2</sub> (001) substrate without adsorbed particle.</p>
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<p>Projected density of states (PDOS) of the α-quartz SiO<sub>2</sub> (001) substrate in different states, (<b>a</b>) no adsorbed particle, (<b>b</b>) adsorbing CH<sub>4</sub>.</p>
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<p>Projected density of states (PDOS) of the α-quartz SiO<sub>2</sub> (001) substrate in different states, (<b>a</b>) no adsorbed particle, (<b>b</b>) adsorbing CH<sub>3</sub>.</p>
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<p>Projected density of states (PDOS) of the α-quartz SiO<sub>2</sub> (001) substrate in different states, (<b>a</b>) no adsorbed particle, (<b>b</b>) adsorbing CH<sub>2</sub>, the adsorption peaks are marked with red circle.</p>
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<p>Projected density of states (PDOS) of the α-quartz SiO<sub>2</sub> (001) substrate in different states, (<b>a</b>) no adsorbed particle, (<b>b</b>) adsorbing CH, the adsorption peaks are marked with red circle.</p>
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<p>Projected density of states (PDOS) of the α-quartz SiO<sub>2</sub> (001) substrate in different states, (<b>a</b>) no adsorbed particle, (<b>b</b>) adsorbing C, the adsorption peaks are marked with red circle.</p>
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<p>Projected density of states (PDOS) of the α-quartz SiO<sub>2</sub> (001) substrate in different states, (<b>a</b>) no adsorbed particle, (<b>b</b>) adsorbing H.</p>
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16 pages, 9046 KiB  
Article
Study on Pore Structure of Tectonically Deformed Coals by Carbon Dioxide Adsorption and Nitrogen Adsorption Methods
by Jinbo Zhang, Huazhou Huang, Wenbing Zhou, Lin Sun and Zaixing Huang
Energies 2025, 18(4), 887; https://doi.org/10.3390/en18040887 - 13 Feb 2025
Viewed by 315
Abstract
The study of pore characteristics in tectonic coal is essential for a deeper understanding of gas diffusion, seepage, and other transport processes within coal seams, and plays a crucial role in the development of coalbed methane resources. Based on low-temperature N2 and [...] Read more.
The study of pore characteristics in tectonic coal is essential for a deeper understanding of gas diffusion, seepage, and other transport processes within coal seams, and plays a crucial role in the development of coalbed methane resources. Based on low-temperature N2 and CO2 adsorption experiments, this study investigated the pore structure characteristics of four tectonic coal samples collected from the Hegang and Jixi basins in China. The results show that the mylonitic coal sample exhibits a clear capillary condensation and evaporation phenomenon around a relative pressure (P/P0) of 0.5. The degree of tectonic deformation in coal has a significant impact on its pore characteristics. As the degree of deformation increases, both the pore volume and specific surface area of the coal gradually increase. The pore volume and specific surface area of micropores are primarily concentrated in pores with diameters of 0.5–0.7 nm and 0.8–0.9 nm, while those of mesopores are mainly distributed in pores with diameters of 2.3–6.2 nm. The proportion of pore volume and specific surface area contributed by micropores is much greater than that of mesopores. The fractal dimension is positively correlated with the degree of tectonic deformation in coal. As the fractal dimension increases, the average pore diameter decreases, closely tied to the destruction and reconstruction of the coal’s pore structure under tectonic stress. These findings will contribute to a deeper understanding of the pore structure characteristics of tectonic coal and effectively advance coalbed methane development. Full article
(This article belongs to the Collection Feature Papers in Carbon Capture, Utilization, and Storage)
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<p>Location map of the study area: (<b>a</b>) outline map of Heilongjiang Province; (<b>b</b>) regional geological structure map of the Hegang Basin; (<b>c</b>) regional geological structure map of the Jixi Basin.</p>
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<p>N<sub>2</sub> adsorption–desorption curves: (<b>a</b>) 1-AN11; (<b>b</b>) 2-YX15; (<b>c</b>) 3-PG14; (<b>d</b>) 4-CS3.</p>
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<p>Adsorption curves of CO<sub>2</sub>.</p>
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<p>Pore structure of micropores: (<b>a</b>) pore volume; (<b>b</b>) specific surface area.</p>
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<p>Pore structure of mesopores: (<b>a</b>) pore volume; (<b>b</b>) specific surface area.</p>
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<p>The pore structure characteristics of tectonically deformed coal: (<b>a</b>) pore volume; (<b>b</b>) pore specific surface area.</p>
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<p>The relationship between tectonic deformation degree and fractal dimension.</p>
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<p>The relationship between pore characteristics of tectonically deformed coal and fractal dimension: (<b>a</b>) the fractal dimension and the pore specific surface area; (<b>b</b>) the fractal dimension and the pore volume; (<b>c</b>) the fractal dimension and the adsorption volume; (<b>d</b>) the fractal dimension and the average pore width.</p>
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20 pages, 6626 KiB  
Article
In Situ N-Doped Low-Corrosion Porous Carbon Derived from Biomass for Efficient CH4/N2 Separation
by Huihui Wang, Yuqiong Zhao, He Lian, Qi Wang, Zhihong Shang and Guojie Zhang
Separations 2025, 12(2), 42; https://doi.org/10.3390/separations12020042 - 8 Feb 2025
Viewed by 314
Abstract
The separation of CH4 and N2 is essential for the effective use of low-concentration coalbed methane (CBM). In this study, a series of nitrogen-doped porous carbons were synthesized using an in situ nitrogen doping method combined with K2CO3 [...] Read more.
The separation of CH4 and N2 is essential for the effective use of low-concentration coalbed methane (CBM). In this study, a series of nitrogen-doped porous carbons were synthesized using an in situ nitrogen doping method combined with K2CO3 activation. The study systematically examined how changes in the physical structure and surface properties of the porous carbons affected their CH4/N2 separation performance. The results revealed that in situ nitrogen doping not only effectively adjusts the pore structure and alters the reaction of K2CO3 on the carbon matrix, but also introduces nitrogen and oxygen functional groups that significantly enhance the adsorption capabilities of the materials. In particular, sample S3Y6−800 demonstrated the highest methane adsorption capacity of 2.23 mmol/g at 273 K and 1 bar, outperforming most other porous carbons. This exceptional performance is attributed to the introduction of N-5, N-6, C-O, and COOH functional groups, as well as a narrower pore-size distribution (0.5–0.7 nm) and the formation of carbon nanotube structures. The introduction of heteroatoms also provides additional adsorption sites for the porous carbon, thus improving its methane adsorption capacity. Furthermore, dynamic breakthrough experiments confirmed that all samples effectively separated methane and nitrogen. The Toth model accurately described the CH4 adsorption behavior on S3Y6−800 at 298 K, suggesting that the adsorption process follows a sub-monolayer coverage mechanism within the microporous regions. This study provides a mild and environmentally friendly preparation method of porous carbons for CH4/N2 separation. Full article
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<p>For all samples: (<b>a</b>) N<sub>2</sub> adsorption–desorption curves; (<b>b</b>) pore size distribution by NLDFT method.</p>
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<p>SEM images of all samples.</p>
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<p>(<b>a</b>) XRD diagrams of samples S<sub>3</sub>Y<sub>6</sub>−700, S<sub>3</sub>Y<sub>6</sub>−800, and S<sub>3</sub>Y<sub>6</sub>−900; (<b>b</b>) FT-IR of samples S<sub>3</sub>Y<sub>6</sub>−700, S<sub>3</sub>Y<sub>6</sub>−800, and S<sub>3</sub>Y<sub>6</sub>−900.</p>
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<p>N1s spectra of (<b>a</b>) S<sub>3</sub>Y<sub>6</sub>−700; (<b>b</b>) S<sub>3</sub>Y<sub>6</sub>−800; and (<b>c</b>) S<sub>3</sub>Y<sub>6</sub>−900.</p>
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<p>O1s spectra of (<b>a</b>) S<sub>3</sub>Y<sub>6</sub>−700; (<b>b</b>) S<sub>3</sub>Y<sub>6</sub>−800; and (<b>c</b>) S<sub>3</sub>Y<sub>6</sub>−900.</p>
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<p>Adsorption isotherms of CH<sub>4</sub> for samples at (<b>a</b>) 273 K and (<b>b</b>) 298 K; adsorption isotherms of N<sub>2</sub> for samples at (<b>c</b>) 273 K and (<b>d</b>) 298 K.</p>
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<p>Correlation between (<b>a</b>) V &lt; 0.6 nm; (<b>b</b>) N5 + N6 + O3 + O4.</p>
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<p>(<b>a</b>) Selectivity of all samples; (<b>b</b>) isosteric heats of adsorption; (<b>c</b>) comparison of CH<sub>4</sub>/N<sub>2</sub> selectivity and CH<sub>4</sub> adsorption capacity.</p>
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<p>Adsorption cycle stability of S<sub>3</sub>Y<sub>6</sub>−800.</p>
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<p>Dynamic permeation curves of samples (<b>a</b>) S<sub>3</sub>Y<sub>6</sub>−700, (<b>b</b>) S<sub>3</sub>Y<sub>6</sub>−800, (<b>c</b>) S<sub>3</sub>Y<sub>6</sub>−900 at CH<sub>4</sub>/N<sub>2</sub> (50/50).</p>
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<p>Fitting of adsorption isotherm mode.</p>
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26 pages, 3664 KiB  
Article
Membrane-Based Hydrogen Production: A Techno-Economic Evaluation of Cost and Feasibility
by Dk Nur Hayati Amali Pg Haji Omar Ali, Hazwani Suhaimi and Pg Emeroylariffion Abas
Hydrogen 2025, 6(1), 9; https://doi.org/10.3390/hydrogen6010009 - 8 Feb 2025
Viewed by 477
Abstract
As the global shift toward a low-carbon economy accelerates, hydrogen is emerging as a crucial energy source. Among conventional methods for hydrogen production, steam methane reforming (SMR), commonly paired with pressure swing adsorption (PSA) for hydrogen purification, stands out due to its established [...] Read more.
As the global shift toward a low-carbon economy accelerates, hydrogen is emerging as a crucial energy source. Among conventional methods for hydrogen production, steam methane reforming (SMR), commonly paired with pressure swing adsorption (PSA) for hydrogen purification, stands out due to its established infrastructure and technological maturity. This comprehensive techno-economic analysis focuses on membrane-based hydrogen production, evaluating four configurations, namely SMR, SMR with PSA, SMR with a palladium membrane, and SMR with a ceramic–carbonate membrane coupled with a carbon capture system (CCS). The life cycle cost (LCC) of each configuration was assessed by analyzing key factors, including production rate, hydrogen pricing, equipment costs, and maintenance expenses. Sensitivity analysis was also conducted to identify major cost drivers influencing the LCC, providing insights into the economic and operational feasibility of each configuration. The analysis reveals that SMR with PSA has the lowest LCC and is significantly more cost-efficient than configurations involving the palladium and ceramic–carbonate membranes. SMR with a ceramic–carbonate membrane coupled with CCS also demonstrates the most sensitive to energy variations due to its extensive infrastructure and energy requirement. Sensitivity analysis confirms that SMR with PSA consistently provides the greatest cost efficiency under varying conditions. These findings underscore the critical balance between cost efficiency and environmental considerations in adopting membrane-based hydrogen production technologies. Full article
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<p>Projected trends in global hydrogen demand across key industries, including transportation, industry, power generation, and refining, by 2050 (<b>a</b>). Projected decline in the demand for gray hydrogen, with a shift toward the adoption of green and blue hydrogen by 2050 (<b>b</b>). (Adapted from [<a href="#B5-hydrogen-06-00009" class="html-bibr">5</a>,<a href="#B6-hydrogen-06-00009" class="html-bibr">6</a>], respectively).</p>
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<p>Block flow diagram of the SMR process with carbon sequestration.</p>
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<p>Cost breakdown of SMR, SMR with PSA, SMR with a palladium membrane, and SMR with a ceramic–carbonate membrane coupled with a CCS.</p>
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<p>Bar chart presentation of LCC for hydrogen production using SMR, SMR with PSA, SMR with a palladium membrane, and SMR with a ceramic–carbonate membrane coupled with a CCS.</p>
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<p>Line chart representation of LCC with energy consumption for SMR with PSA, SMR with a palladium membrane, and SMR with a ceramic–carbonate membrane coupled with a CCS.</p>
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<p>Tornado chart representation of key parameters in sensitivity analysis.</p>
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<p>Effect of varying the hydrogen price and its production rate on the LCC of SMR with PSA, SMR with a palladium membrane, and SMR with a ceramic–carbonate membrane coupled with a CCS.</p>
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<p>Effect of varying the hydrogen price and its production rate on the LCC of SMR.</p>
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<p>Effect of varying the equipment and maintenance costs on the LCC of SMR with PSA, SMR with a palladium membrane, and SMR with a ceramic–carbonate membrane coupled with a CCS.</p>
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<p>Effect of varying the discount rate on the LCC of SMR with PSA, SMR with a palladium membrane, and SMR with a ceramic–carbonate membrane coupled with a CCS.</p>
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17 pages, 3863 KiB  
Article
Adsorption Pore Volume Distribution Heterogeneity of Middle and High Rank Coal Reservoirs and Determination of Its Influencing Factors
by Kai Wang, Fangkai Quan, Shizhao Zhang, Yubo Zhao, He Shi, Tingting Yin and Zhenyuan Qin
Processes 2025, 13(2), 429; https://doi.org/10.3390/pr13020429 - 6 Feb 2025
Viewed by 385
Abstract
Heterogeneity of adsorption pore volume distribution affects desorption and diffusion processes of coal reservoirs. In this paper, N2 and CO2 adsorption and desorption experiment tests were used to study the pore structure of middle and high rank coal reservoirs in the [...] Read more.
Heterogeneity of adsorption pore volume distribution affects desorption and diffusion processes of coal reservoirs. In this paper, N2 and CO2 adsorption and desorption experiment tests were used to study the pore structure of middle and high rank coal reservoirs in the study area. The fractal theory of volume and surface area is used to achieve a full-scale fractal study of adsorption pores (pore diameter is less than 100 nm) in the study area. Firstly, adaptability and control factors of volume fractals and surface area fractals within the same aperture scale range are studied. Secondly, fractal characteristics of micro-pores and meso-pores are studied. Thirdly, fractal characteristics within different aperture scales and the influencing factors of fractal characteristics within different scale ranges are studied. The results are as follows. With the increase in coal rank, pore volume and specific surface area of pores less than 0.8 nm increase, and dominant pore size changes from 0.55~0.8 nm (middle coal rank) to 0.5~0.7 nm (high coal rank). As coal rank increases, TPV and average pore diameter (APD) decrease under the BJH model, while SSA changes are not significant under the BET model. Moreover, as the pore diameter decreases, the correlation between the integral dimension of pore volume and degree of coal metamorphism decreases. This result can provide a theoretical basis for the precise characterization of the target coal seam pore and fracture structure and support the optimization of favorable areas for coalbed methane. Full article
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<p>Hysteresis loop types and pore connectivity and shapes by using LP N<sub>2</sub> GA tests.</p>
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<p>Hysteresis loop types and pore connectivity and shapes by using LP N<sub>2</sub> GA tests.</p>
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<p>Pore size distributions: (<b>a</b>,<b>b</b>) Incremented pore volume vs. APD of the different series samples, (<b>c</b>,<b>d</b>) Incremented specific surface area vs. APD of the different series.</p>
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<p>Pore size distributions: (<b>a</b>,<b>b</b>) Incremented pore volume vs. APD of the different series samples, (<b>c</b>,<b>d</b>) Incremented specific surface area vs. APD of the different series.</p>
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<p>Adsorption curves of CO<sub>2</sub> adsorption for different rank samples.</p>
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<p>Incremental pore volume plots of DFT-microporous for the middle and high coals. (<b>a</b>,<b>b</b>) Incremental pore volume plots of DFT-microporous for the middle coals, Incremental pore volume plots of DFT-microporous for the high coals.</p>
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<p>Incremental surface areas plots of DFT-microporous for the middle and high coals. (<b>a</b>,<b>b</b>) Incremental surface areas plots of DFT-microporous for the middle coals, Incremental surface areas plots of DFT-microporous for the high coals.</p>
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<p>Plots of ln V versus ln(ln(<span class="html-italic">P</span><sub>0</sub>/<span class="html-italic">P</span>)) based on gas adsorption isotherms for the 9 coal specimens.</p>
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<p>Surface fractal curve of the sample (HG/DHS/HF/SB) based on the LPN<sub>2</sub> GA.</p>
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<p>Plots of ln(<span class="html-italic">S</span>) versus ln(<span class="html-italic">r</span>) based on gas adsorption isotherms for the typical coal specimens.</p>
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<p>Volume fractal curve of the micro-pores through Sierpinski model (<b>a</b>,<b>b</b>), the volume fractal dimension of <span class="html-italic">D<sub>av</sub></span><sub>1</sub> and <span class="html-italic">D<sub>av</sub></span><sub>2</sub> of all the samples (<b>c</b>).</p>
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<p>Surface fractal curve of the micro-pores (<b>a</b>,<b>b</b>), the surface fractal dimension of <span class="html-italic">D<sub>as</sub></span><sub>1</sub>/<span class="html-italic">D<sub>as</sub></span><sub>2</sub> and <span class="html-italic">D<sub>as</sub></span><sub>3</sub> of all the samples (<b>c</b>).</p>
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<p>The relationship between volume fractal and surface fractal of adsorption pores based on LPN2/CO<sub>2</sub> GA (<b>a</b>): the relationship between volume fractal and surface fractal; (<b>b</b>): the relationship between volume fractal and surface fractal; (<b>c</b>): the relationship between volume fractal and surface fractal; (<b>d</b>): the relationship between volume fractal and surface fractal.</p>
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<p>The relationship between vitrinite reflectance and each fractal dimension (<b>a</b>,<b>b</b>): the relationship between adsorption pore volume/surface area fractal and R<sub>0</sub>; (<b>c</b>,<b>d</b>): the relationship between micropore volume/surface area fractal and R<sub>0</sub>.</p>
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<p>The relationship between vitrinite reflectance and each fractal dimension (<b>a</b>,<b>b</b>): the relationship between adsorption pore volume/surface area fractal and R<sub>0</sub>; (<b>c</b>,<b>d</b>): the relationship between micropore volume/surface area fractal and R<sub>0</sub>.</p>
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<p>The relationship between volatile matter/fixed carbon and all the fractal dimensions. (<b>a</b>,<b>b</b>) The relationship between the volatile matter of sample <span class="html-italic">D<sub>v</sub></span><sub>1</sub>, <span class="html-italic">D<sub>v</sub></span><sub>2</sub> and all fractal dimensions, The relationship between the volatile matter of sample <span class="html-italic">D<sub>av</sub></span><sub>1</sub>, <span class="html-italic">D<sub>av</sub></span><sub>2</sub> and all fractal dimensions. (<b>c</b>,<b>d</b>) The relationship between the fixed carbon of sample <span class="html-italic">D<sub>v</sub></span><sub>1</sub>, <span class="html-italic">D<sub>v</sub></span><sub>2</sub> and all fractal dimensions, The relationship between the fixed carbon of sample <span class="html-italic">D<sub>av</sub></span><sub>1</sub>, <span class="html-italic">D<sub>av</sub></span><sub>2</sub> and all fractal dimensions.</p>
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15 pages, 3077 KiB  
Article
Gas Content and Gas Occurrence Mechanism of Deep Coal Seams in the Shenfu-Linxing Block
by Litao Ma, Fan Yang, Jianghao Yang, Yi Cui, Wei Wang, Cheng Liu, Bo Zhang, Jiang Yang and Shu Tao
Energies 2025, 18(3), 699; https://doi.org/10.3390/en18030699 - 3 Feb 2025
Viewed by 512
Abstract
The Shenfu-Linxing block in the Ordos Basin holds abundant deep coalbed methane (CBM) resources, which can alleviate gas shortages and aid dual carbon target achievement. Considering isothermal adsorption traits and parameters like vitrinite reflectance, temperature, pressure, and water saturation, a prediction model for [...] Read more.
The Shenfu-Linxing block in the Ordos Basin holds abundant deep coalbed methane (CBM) resources, which can alleviate gas shortages and aid dual carbon target achievement. Considering isothermal adsorption traits and parameters like vitrinite reflectance, temperature, pressure, and water saturation, a prediction model for adsorbed and free gas content was formulated. This model helps to reveal the deep CBM occurrence mechanism in the Shenfu-Linxing block. Results show that deep CBM exists in both adsorbed and free states, with adsorbed gas initially increasing then decreasing, and free gas rising then stabilizing as burial depth increases. A critical transition depth for total CBM content exists, shallowing with higher water saturation. As depth increases, temperature and pressure evolution results in a “rapid growth—slow growth—stability—slow decrease” pattern in total gas content. Adsorbed gas resides in micropores, while free gas occupies larger pores. Full article
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<p>(<b>a</b>) Location of the Ordos Basin; (<b>b</b>) Tectonic units of the Ordos Basin; (<b>c</b>) Division of eastern margin of Ordos Basin and the location of Shenfu-Linxing block (modified from [<a href="#B37-energies-18-00699" class="html-bibr">37</a>]).</p>
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<p>Lower Permian stratigraphic column in the Shenfu-Linxing block (modified from [<a href="#B37-energies-18-00699" class="html-bibr">37</a>]).</p>
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<p>Characteristic diagram of isothermal adsorption curves under different temperatures (30 °C, 50 °C, 70 °C) and pressure conditions in the Shenfu-Linxing block.</p>
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<p>(<b>a</b>) Relationship between Langmuir pressure and R<sub>o, max</sub>; (<b>b</b>) Relationship between Langmuir pressure and temperature; (<b>c</b>) Relationship between Langmuir volume and R<sub>o, max</sub>; (<b>d</b>) Relationship between Langmuir volume and temperature.</p>
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<p>Variation of free gas content with depth.</p>
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<p>Variation of gas content and critical transition depth with burial depth of coal seam under different water saturation conditions.</p>
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<p>Comparison of adsorption capacity of micropores and mesopores with Langmuir volume (left: Hypothesis I, right: Hypothesis II).</p>
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<p>Phase evolution model of deep CBM (modified from [<a href="#B15-energies-18-00699" class="html-bibr">15</a>]).</p>
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19 pages, 5031 KiB  
Article
Fractal Characterization and Pore Evolution in Coal Under Tri-Axial Cyclic Loading–Unloading: Insights from Low-Field NMR Imaging and Analysis
by Zelin Liu, Senlin Xie, Yajun Yin and Teng Su
Fractal Fract. 2025, 9(2), 93; https://doi.org/10.3390/fractalfract9020093 - 1 Feb 2025
Viewed by 486
Abstract
Coal resource extraction and utilization are essential for sustainable development and economic growth. This study integrates a pseudo-triaxial mechanical loading system with low-field nuclear magnetic resonance (NMR) to enable the preliminary visualization of coal’s pore-fracture structure (PFS) under mechanical stress. Pseudo-triaxial and cyclic [...] Read more.
Coal resource extraction and utilization are essential for sustainable development and economic growth. This study integrates a pseudo-triaxial mechanical loading system with low-field nuclear magnetic resonance (NMR) to enable the preliminary visualization of coal’s pore-fracture structure (PFS) under mechanical stress. Pseudo-triaxial and cyclic loading–unloading tests were combined with real-time NMR monitoring to model porosity recovery, pore size evolution, and energy dissipation, while also calculating the fractal dimensions of pores in relation to stress. The results show that during the compaction phase, primary pores are compressed with limited recovery after unloading. In the elastic phase, both adsorption and seepage pores transform significantly, with most recovering post-unloading. After yield stress, new fractures and pores form, and unloading enhances fracture connectivity. Seepage pore porosity shows a negative exponential relationship with axial strain before yielding, and a logarithmic relationship afterward. The fractal dimension of adsorption pores decreases during compaction and increases afterward, while the fractal dimension of seepage pores decreases before yielding and increases post-yielding. These findings provide new insights into the flow patterns of methane in coal seams. Full article
(This article belongs to the Special Issue Fractal Dimensions with Applications in the Real World)
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<p>Online pseudo-triaxial loading experimental system with nuclear magnetic resonance: (<b>a</b>) image of the main devices; (<b>b</b>) schematic representation of core holder. (Reproduced with permission from [<a href="#B38-fractalfract-09-00093" class="html-bibr">38</a>]. Copyright 2023 Elsevier).</p>
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<p>Online pseudo-triaxial loading experimental system with nuclear magnetic resonance: (<b>a</b>) image of the main devices; (<b>b</b>) schematic representation of core holder. (Reproduced with permission from [<a href="#B38-fractalfract-09-00093" class="html-bibr">38</a>]. Copyright 2023 Elsevier).</p>
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<p>Schematic view of stress redistribution during coal mining operations.</p>
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<p>Schematic diagram of <span class="html-italic">T</span><sub>2</sub> spectrum.</p>
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<p>Spatial distribution evolution of pores in coal: (<b>a</b>) triaxial loading; (<b>b</b>) triaxial loading and unloading.</p>
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<p>Pore size distribution in coal under initial hydrostatic stress.</p>
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<p>Dimensionless content ratios of various pore types under varying axial strains.</p>
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<p>Evolution of porosity recovery rates during triaxial cyclic loading–unloading: (<b>a</b>) adsorption pores; (<b>b</b>) seepage pores and fractures; (<b>c</b>) total pores.</p>
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<p><span class="html-italic">T</span><sub>2g</sub> in coal samples under different axial strains.</p>
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<p>Evolution of <span class="html-italic">T</span><sub>2g</sub> recovery rates during cyclic loading–unloading.</p>
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<p>Evolution in the fractal dimension: (<b>a</b>) triaxial loading; (<b>b</b>) triaxial loading and unloading.</p>
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<p>Evolution in the total energy, dissipated energy, and dissipated energy ratio under cyclic loading–unloading.</p>
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<p>Relationship between porosity and axial strain: (<b>a</b>) at the loading stage; (<b>b</b>) at the unloading stage.</p>
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21 pages, 3998 KiB  
Article
Solubility and Diffusion of Main Biogas Components in a Glassy Polysulfone-Based Membrane
by Marek Tańczyk, Aleksandra Janusz-Cygan, Anna Pawlaczyk-Kurek, Łukasz Hamryszak, Jolanta Jaschik and Katarzyna Janusz-Szymańska
Molecules 2025, 30(3), 614; https://doi.org/10.3390/molecules30030614 - 30 Jan 2025
Viewed by 456
Abstract
Biogas, one of the important controllable renewable energy sources, may be split into two streams: bio-CH4 and bio-CO2 using, among others, membrane processes. The proper optimization of such processes requires the knowledge of phenomena accompanying each specific CH4–CO2 [...] Read more.
Biogas, one of the important controllable renewable energy sources, may be split into two streams: bio-CH4 and bio-CO2 using, among others, membrane processes. The proper optimization of such processes requires the knowledge of phenomena accompanying each specific CH4–CO2–membrane system (e.g., competitive sorption or swelling). The phenomena were analyzed for the polysulfone-based membrane used in a developed adsorptive–membrane system for biogas separation. The Dual Mode Sorption and partial immobilization models were used to describe the solubility and diffusion of CO2, CH4 and their mixtures in this material. The parameters of the models were determined based on pure-gas sorption isotherms measured gravimetrically and permeances of CO2/CH4 mixture components from our previous studies. It was found, among other things, that the membrane swelling caused by CO2 was observed for pressures higher than 5 bar. The real selectivity (permselectivity) of CO2 vs. CH4 is significantly lower than the selectivity of pure gases (ideal selectivity), while the solubility selectivity of CO2 vs. CH4 in the mixture is higher than that of pure gases. This is due to the better affinity of CO2 towards the tested polysulfone membrane, making CO2 the dominant component in competitive sorption. The reduction in the permselectivity is mainly due to an approximately two-fold decrease in the CO2 diffusion rate in the presence of CH4. It was also found that the fraction of solubility in the fractional free volume (FFV) is dominant for both gases, pure and mixed, reaching 65–73% of the total solubility. Moreover, in CO2/CH4 mixtures, the mobility of methane in FFV disappears, which additionally confirms the displacement of methane by CO2 from FFV. Full article
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<p>Biomethane value chain.</p>
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<p>Sample mass uptake curves of pure (<b>a</b>) CO<sub>2</sub> and (<b>b</b>) CH<sub>4</sub> in the polysulfone-based membrane from Air Products’ PRISM PA1020–P1 module.</p>
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<p>Experimental solubility of carbon dioxide and methane in the polysulfone-based membrane from Air Products’ PRISM PA1020–P1 module at 293 K.</p>
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<p>Solubility of pure (<b>a</b>) CO<sub>2</sub> and (<b>b</b>) CH<sub>4</sub> in the polysulfone-based membrane from Air Products’ PRISM PA1020–P1 module. Points represent experimental data and lines Dual Mode Sorption (DMS) model predictions (for minimized solubility squared differences).</p>
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<p>Mass change due to sorption (solid lines) and desorption (dashed lines) of CO<sub>2</sub> at 293 K concerning (<b>a</b>) two isothermal runs in pressure range of 0–5 bar (green) and 0–10 bar (red), pressure below 5 bar, (<b>b</b>) isothermal run in pressure range of 0–10 bar, pressure above 5 bar.</p>
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<p>Total and FFV solubility of (<b>a</b>) CO<sub>2</sub> and (<b>b</b>) CH<sub>4</sub>, pure and mixed (CO<sub>2</sub>: 50 vol.%/CH<sub>4</sub>: 50 vol.%) at 295 K according to Dual Mode Sorption (DMS) model predictions for minimized concentration (blue) and solubility (green) squared differences.</p>
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<p>Comparison of solubility (solid lines) and permeance (solid points with dotted lines) of pure and mixed CO<sub>2</sub> (blue) and CH<sub>4</sub> (green) for the mixture of (<b>a</b>) CO<sub>2</sub> (50 vol.%)/CH<sub>4</sub> (50 vol.%) and (<b>b</b>) CO<sub>2</sub> (40 vol.%)/CH<sub>4</sub> (60 vol.%). Solubility was calculated using the DMS model with minimized solubility squared differences.</p>
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<p>Comparison of pure and mixed diffusivity at 295 K for CO<sub>2</sub> (blue color) and CH<sub>4</sub> (green color) in the case of the DMS model with minimized solubility squared differences.</p>
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<p>Comparison of total (green), solubility (red) and diffusivity (blue) CO<sub>2</sub> vs. CH<sub>4</sub> selectivity, pure (solid lines) and mixed (dashed lines), for the mixture of (<b>a</b>) CO<sub>2</sub> (50 vol.%)/CH<sub>4</sub> (50 vol.%) and (<b>b</b>) CO<sub>2</sub> (40 vol.%)/CH<sub>4</sub> (60 vol.%). The case of the DMS model with minimized solubility squared differences.</p>
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21 pages, 5430 KiB  
Article
Electrocatalytic Pathways and Efficiency of Cuprous Oxide (Cu2O) Surfaces in CO2 Electrochemical Reduction (CO2ER) to Methanol: A Computational Approach
by Zubair Ahmed Laghari, Wan Zaireen Nisa Yahya, Sulafa Abdalmageed Saadaldeen Mohammed and Mohamad Azmi Bustam
Catalysts 2025, 15(2), 130; https://doi.org/10.3390/catal15020130 - 29 Jan 2025
Viewed by 764
Abstract
Carbon dioxide (CO2) can be electrochemically, thermally, and photochemically reduced into valuable products such as carbon monoxide (CO), formic acid (HCOOH), methane (CH4), and methanol (CH3OH), contributing to carbon footprint mitigation. Extensive research has focused on catalysts, [...] Read more.
Carbon dioxide (CO2) can be electrochemically, thermally, and photochemically reduced into valuable products such as carbon monoxide (CO), formic acid (HCOOH), methane (CH4), and methanol (CH3OH), contributing to carbon footprint mitigation. Extensive research has focused on catalysts, combining experimental approaches with computational quantum mechanics to elucidate reaction mechanisms. Although computational studies face challenges due to a lack of accurate approximations, they offer valuable insights and assist in selecting suitable catalysts for specific applications. This study investigates the electrocatalytic pathways of CO2 reduction on cuprous oxide (Cu2O) catalysts, utilizing the computational hydrogen electrode (CHE) model based on density functional theory (DFT). The electrocatalytic performance of flat Cu2O (100) and hexagonal Cu2O (111) surfaces was systematically analysed, using the standard hydrogen electrode (SHE) as a reference. Key parameters, including free energy changes (ΔG), adsorption energies (Eads), reaction mechanisms, and pathways for various intermediates were estimated. The results showed that CO2 was reduced to CO(g) on both Cu2O surfaces at low energies. However, methanol (CH3OH) production was observed preferentially on Cu2O (111) at ΔG = −1.61 eV, whereas formic acid (HCOOH) and formaldehyde (HCOH) formation were thermodynamically unfavourable at interfacial sites. The CO2-to-methanol conversion on Cu2O (100) exhibited a total ΔG of −3.38 eV, indicating lower feasibility compared to Cu2O (111) with ΔG = −5.51 eV. These findings, which are entirely based on a computational approach, highlight the superior catalytic efficiency of Cu2O (111) for methanol synthesis. This approach also holds the potential for assessing the catalytic performance of other transition metal oxides (e.g., nickel oxide, cobalt oxide, zinc oxide, and molybdenum oxide) and their modified forms through doping or alloying with various elements. Full article
(This article belongs to the Special Issue Catalysis for CO2 Conversion, 2nd Edition)
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<p>The representations following activation and interaction of the Cu<sub>2</sub>O (100) site with different adsorbates after geometry optimization; (<b>a</b>) original dimension of Cu<sub>2</sub>O (100) and structures of adsorbates, (<b>b</b>) *CO<sub>2</sub>, (<b>c</b>) *HCOO, (<b>d</b>) *COOH, (<b>e</b>) *CO, (<b>f</b>) *HCOOH, (<b>g</b>) *HCOH, (<b>h</b>) *H<sub>3</sub>CO, and (<b>i</b>) *CH<sub>3</sub>OH.</p>
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<p>The representations following activation and interaction of the Cu<sub>2</sub>O (111) site with different adsorbates after geometry optimization; (<b>a</b>) Original dimension of Cu<sub>2</sub>O (111) and structures of adsorbates, (<b>b</b>) *CO<sub>2</sub>, (<b>c</b>) *HCOO, (<b>d</b>) *COOH, (<b>e</b>) *CO, (<b>f</b>) *HCOOH, (<b>g</b>) *HCOH, (<b>h</b>) *H<sub>3</sub>CO, and (<b>i</b>) *CH<sub>3</sub>OH.</p>
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<p>Adsorption energies of different adsorbates on Cu<sub>2</sub>O (100) and Cu<sub>2</sub>O (111).</p>
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<p>The free energy (ΔG) diagram for various products over Cu<sub>2</sub>O (100), and Cu<sub>2</sub>O (111) surfaces.</p>
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<p>The two different pathways for CO<sub>2</sub>ER to CH<sub>3</sub>OH over the Cu<sub>2</sub>O (100) and Cu<sub>2</sub>O (111) surfaces.</p>
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<p>The suggested CO<sub>2</sub> reduction pathways to CO<sub>(g)</sub>, HCOOH<sub>(l)</sub>, H<sub>2</sub>CO<sub>(l)</sub>, and CH<sub>3</sub>OH. The red arrows indicate the desorption pathways for different products.</p>
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34 pages, 3971 KiB  
Review
Microporous Adsorbents for CH4 Capture and Separation from Coalbed Methane with Low CH4 Concentration: Review
by Xiao Wei, Yingkai Xia, Shuang Wei, Yuehui Chen and Shaobin Yang
Nanomaterials 2025, 15(3), 208; https://doi.org/10.3390/nano15030208 - 28 Jan 2025
Viewed by 498
Abstract
A rapid increase in natural gas consumption has resulted in a shortage of conventional natural gas resources, while an increasing concentration of CH4 in the atmosphere has intensified the greenhouse effect. The exploration and utilization of coalbed methane (CBM) resources not only [...] Read more.
A rapid increase in natural gas consumption has resulted in a shortage of conventional natural gas resources, while an increasing concentration of CH4 in the atmosphere has intensified the greenhouse effect. The exploration and utilization of coalbed methane (CBM) resources not only has the potential to fill the gap in natural gas supply and promote the development of green energy, but could also reduce CH4 emissions into the atmosphere and alleviate global warming. However, the efficient separation of CH4 and N2 has become a significant challenge in the utilization of CBM, which has attracted significant attention from researchers in recent years. The development of efficient CH4/N2 separation technologies is crucial for enhancing the exploitation and utilization of low-concentration CBM and is of great significance for sustainable development. In this paper, we provide an overview of the current methods for CH4/N2 separation, summarizing their respective advantages and limitations. Subsequently, we focus on reviewing research advancements in adsorbents for CH4/N2 separation, including zeolites, metal–organic frameworks (MOFs), and porous carbon materials. We also analyze the relationship between the pore structure and surface properties of these adsorbents and their adsorption separation performances, and summarize the challenges and difficulties that different types of adsorbents face in their future development. In addition, we also highlight that matching the properties of adsorbents and adsorbates, controlling pore structures, and tuning surface properties on an atomic scale will significantly increase the potential of adsorbents for CH4 capture and separation from CBM. Full article
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<p>Energy distribution of (<b>a</b>) CH<sub>4</sub> and (<b>b</b>) N<sub>2</sub> during adsorption in Si(1)Al(1) clusters [<a href="#B46-nanomaterials-15-00208" class="html-bibr">46</a>].</p>
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<p>Structures of (<b>a</b>) AlPO<sub>4</sub>−17 (ERI), (<b>b</b>) AlPO<sub>4</sub>−18 (AEI), (<b>c</b>) AlPO<sub>4</sub>−33 (ATT), and (<b>d</b>) UiO−7 (ZON) [<a href="#B47-nanomaterials-15-00208" class="html-bibr">47</a>].</p>
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<p>Adsorption and desorption isotherms of N<sub>2</sub> at 77 K (<b>a</b>); mesopore distribution (DFT model in the left region; BJH model in the right region. Both are based on adsorption branching curves) (<b>b</b>); histogram of specific surface area (<b>c</b>); and histogram of pore volume (<b>d</b>) [<a href="#B55-nanomaterials-15-00208" class="html-bibr">55</a>].</p>
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<p>View of the unit cell of the M/DOBDC (Mg, Co, or Ni) structure (<b>a</b>) and the MIL−100(Cr) structure (<b>b</b>) (M, blue; O, red; C, gray; H, white) [<a href="#B66-nanomaterials-15-00208" class="html-bibr">66</a>].</p>
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<p>The self-assembly processes through (<b>a</b>) helical chains of AlO<sub>6</sub> polyhedra with m-BDC and FDC linkers; (<b>b</b>) linear chains of AlO<sub>6</sub> polyhedra with FA and p-BDC linkers. (<b>c</b>) Square-like and rhombic-like 1D channels in Al-MOFs viewed along the c axis and a axis, respectively. (<b>d</b>) The PXRD patterns of Al-MOFs; (<b>e</b>) N<sub>2</sub> adsorption–desorption isotherms of Al-MOFs at 77 K (filled and open symbols represent adsorption and desorption, respectively). (<b>f</b>) Pore size distribution of Al-MOFs calculated by HK method [<a href="#B69-nanomaterials-15-00208" class="html-bibr">69</a>].</p>
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<p>(<b>a</b>–<b>d</b>) The ligands used for the construction of Ni-MOFs. (<b>e</b>–<b>l</b>) Pore aperture and pore chemistry for the family of porous materials. Comparison of guest accessible channels of (<b>m</b>) Ni(ina)<sub>2</sub>, (<b>n</b>) Ni(3-ain)<sub>2</sub>, (<b>o</b>) Ni(2-ain)<sub>2</sub>, and (<b>p</b>) Ni(pba)<sub>2</sub> [<a href="#B77-nanomaterials-15-00208" class="html-bibr">77</a>].</p>
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<p>A view of the 2D and 3D structures of the Cu(4,4′-bpy)<sub>2</sub>(OTf)<sub>2</sub> frameworks [<a href="#B81-nanomaterials-15-00208" class="html-bibr">81</a>].</p>
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<p>SEM images of coal-based porous carbons: (<b>a</b>) C-RC, (<b>b</b>) C-TC1.00, (<b>c</b>) C-TC1.25, (<b>d</b>) C-TC1.50, and (<b>e</b>–<b>g</b>) TEM images of CNTs in C-TC1.25 [<a href="#B98-nanomaterials-15-00208" class="html-bibr">98</a>].</p>
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<p>Schematic illustration for fabricating nitrogen-doped activated carbon adsorbent from waste banana peels and grapefruit peels [<a href="#B103-nanomaterials-15-00208" class="html-bibr">103</a>].</p>
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<p>The relationship between the limit adsorption of CH<sub>4</sub> and pore structure parameters for nNi/C series adsorbents (<b>a</b>); schematic diagram of Ni-decorated porous carbon composites enhancing CH<sub>4</sub> adsorption (<b>b</b>) [<a href="#B115-nanomaterials-15-00208" class="html-bibr">115</a>].</p>
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<p>Schematic illustration of the synthesis of porous carbon adsorbents [<a href="#B117-nanomaterials-15-00208" class="html-bibr">117</a>].</p>
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25 pages, 18430 KiB  
Article
Pore Structure and Heterogeneity Characteristics of Deep Coal Reservoirs: A Case Study of the Daning–Jixian Block on the Southeastern Margin of the Ordos Basin
by Bo Li, Yanqin Guo, Xiao Hu, Tao Wang, Rong Wang, Xiaoming Chen, Wentian Fan and Ze Deng
Minerals 2025, 15(2), 116; https://doi.org/10.3390/min15020116 - 24 Jan 2025
Viewed by 445
Abstract
To clarify the micropore structure and fractal characteristics of the Danning–Jixian block on the eastern margin of the Ordos Basin, this study focuses on the deep coal rock of the Benxi Formation in that area. On the basis of an analysis of coal [...] Read more.
To clarify the micropore structure and fractal characteristics of the Danning–Jixian block on the eastern margin of the Ordos Basin, this study focuses on the deep coal rock of the Benxi Formation in that area. On the basis of an analysis of coal quality and physical properties, qualitative and quantitative studies of pore structures with different pore diameters were conducted via techniques such as field emission scanning electron microscopy (FE-SEM), low-pressure CO2 adsorption (LP-CO2A), low-temperature N2 adsorption (LT-N2A), and high-pressure mercury intrusion (HPMI). By applying fractal theory and integrating the results from the LP-CO2A, LT-N2A, and HPMI experiments, the fractal dimensions of pores with different diameters were obtained to characterize the complexity and heterogeneity of the pore structures of the coal samples. The results indicate that the deep coal reservoirs in the Danning–Jixian block have abundant nanometer-scale organic matter gas pores, tissue pores, and a small number of intergranular pores, showing strong heterogeneity influenced by the microscopic components and forms of distribution of organic matter. The pore structure of the Benxi Formation exhibits significant cross-scale effects and strong heterogeneity and is predominantly composed of micropores that account for more than 90% of the total pore volume; the pore structure is affected mainly by the degree of coalification, the vitrinite group, and the ash yield. Fractal analysis reveals that the heterogeneity of macropores is greater than that of mesopores and micropores. This may be attributed to the smaller pore sizes and concentrated distributions of micropores, which are less influenced by diagenesis, resulting in simpler pore structures with lower fractal dimensions. In contrast, mesopores and macropores, with larger diameters and broader distributions, exhibit diverse origins and are more affected by diagenesis, reflecting strong heterogeneity. The abundant storage space and strong self-similarity of micropores in deep coal facilitate the occurrence, flow, and extraction of deep coalbed methane. Full article
(This article belongs to the Special Issue Characterization of Geological Material at Nano- and Micro-scales)
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<p>Regional location and coal-bearing strata of the Daning–Jixian block [<a href="#B43-minerals-15-00116" class="html-bibr">43</a>]. (<b>a</b>) Location of the study area; (<b>b</b>) Tectonic location of the Daning-Jixian block; (<b>c</b>) General stratigraphic colu.</p>
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<p>Schematic illustration of the sample preparation methods [<a href="#B11-minerals-15-00116" class="html-bibr">11</a>].</p>
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<p>Porosity and permeability of deep coal samples from the Benxi Formation in the study area.</p>
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<p>FE-SEM images of various pores in deep coal samples from the Benxi Formation in the study area. (<b>a</b>) DJ1-3, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 2.08%; (<b>b</b>) DJ2-2, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 2.13%; (<b>c</b>) P-2, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 3.04%; (<b>d</b>) DJ2-2, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 2.13%; (<b>e</b>) DJ2-3, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 2.19%; (<b>f</b>) DJ2-2, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 2.13%; (<b>g</b>) P-1, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 3.05%; (<b>h</b>) DJ1-4, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 2.20%; (<b>i</b>) DJ2-3, <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> = 2.19%.</p>
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<p>HPMI (<b>a</b>), LT-N<sub>2</sub>A (<b>b</b>), and LP-CO<sub>2</sub>A (<b>c</b>) curves of deep coal samples from the Benxi Formation in the study area.</p>
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<p>PV distribution curves of deep coal samples from the Benxi Formation in the study area. (<b>a</b>) HPMI; (<b>b</b>) LT-N<sub>2</sub>A; (<b>c</b>) LP-CO<sub>2</sub>A.</p>
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<p>SSA distribution curves of deep coal samples from the Benxi Formation in the study area. (<b>a</b>) HPMI; (<b>b</b>) LT-N<sub>2</sub>A; (<b>c</b>) LP-CO<sub>2</sub>A.</p>
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<p>Fractal fitting of deep coal samples from the Benxi Formation in the study area. (<b>a</b>) HPMI; (<b>b</b>) LT-N<sub>2</sub>A; (<b>c</b>) LP-CO<sub>2</sub>A.</p>
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<p>Principle of the dominant pore size segments for LP-CO<sub>2</sub>A, LT-N<sub>2</sub>A, and HPMI [<a href="#B11-minerals-15-00116" class="html-bibr">11</a>].</p>
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<p>Characteristics of the PV distribution of deep coal samples from the Benxi Formation in the study area. (<b>a</b>) DJ1; (<b>b</b>) DJ2; (<b>c</b>) P.</p>
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<p>Characteristics of the SSA distribution of deep coal samples from the Benxi Formation in the study area. (<b>a</b>) DJ1; (<b>b</b>) DJ2; (<b>c</b>) P.</p>
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<p>Distributions of different scales of pores acquired from LP-CO<sub>2</sub>A, LT-N<sub>2</sub>A, and HPMI. (<b>a</b>) Percentage of PV; (<b>b</b>) percentage of SSA.</p>
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<p>Relationships between the <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub>, vitrinite content, mineral content, and pore structure parameters of deep Benxi Formation coal samples. (<b>a</b>) <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> vs. PV and SSA; (<b>b</b>) vitrinite content vs. PV and SSA; (<b>c</b>) mineral content vs. PV and SSA.</p>
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<p>Relationships between <span class="html-italic">A<sub>d</sub></span>, <span class="html-italic">FC<sub>ad</sub></span>, and pore structure parameters of deep coal samples from the Benxi Formation. (<b>a</b>) <span class="html-italic">A<sub>d</sub></span> vs. PV and SSA; (<b>b</b>) <span class="html-italic">FC<sub>ad</sub></span> vs. PV and SSA.</p>
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<p>Relationships between the <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub>, vitrinite content, <span class="html-italic">A<sub>d</sub></span>, and fractal dimension of deep coal samples from the Benxi Formation. (<b>a</b>) <span class="html-italic">R<sub>o</sub></span><sub>,<span class="html-italic">max</span></sub> vs. fractal dimension; (<b>b</b>) vitrinite content vs. fractal dimension; (<b>c</b>) <span class="html-italic">A<sub>d</sub></span> vs. fractal dimension.</p>
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<p>Relationships between the PV, SSA, and fractal dimension of deep coal samples from the Benxi Formation. (<b>a</b>) Macropore PV and SSA vs. <span class="html-italic">D</span><sub>1</sub>; (<b>b</b>) mesopore PV and SSA vs. <span class="html-italic">D</span><sub>2</sub>; (<b>c</b>) micropore PV and SSA vs. <span class="html-italic">D</span><sub>3</sub>.</p>
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<p>Relationships between pore structure parameters and fractal dimensions of deep coal samples from the Benxi Formation. (<b>a</b>) PV vs. fractal dimension; (<b>b</b>) SSA vs. fractal dimension.</p>
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